{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f36b2b5",
   "metadata": {},
   "source": [
    "# Изучаю Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5af515",
   "metadata": {},
   "source": [
    "<strong>Цель:</strong> хочу уметь прогнозировать поведение субъектов для повышения количества продаж продуктов, для повышения вовлеченности пользователей.\n",
    "\n",
    "Созданию понимание на базе открытых <a href=\"https://www.youtube.com/watch?v=7eh4d6sabA0&ab_channel=ProgrammingwithMosh\">курсов</a>. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a24b436a",
   "metadata": {},
   "source": [
    "### Обучение и прогнозирование"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d920a6d6",
   "metadata": {},
   "source": [
    "У нас есть конечный набор входных и выходных данных. Мы хотим предсказать результат выходных данных и их точноть по новым, полученным из вне, опираясь на готовую модель."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d629f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354df1bb",
   "metadata": {},
   "source": [
    "Считываем данные из .csv файла и выводим парочку для понимания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "36ae3ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>HipHop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>HipHop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>HipHop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>Jazz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender   genre\n",
       "0   20       1  HipHop\n",
       "1   23       1  HipHop\n",
       "2   25       1  HipHop\n",
       "3   26       1    Jazz\n",
       "4   29       1    Jazz"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "music_data = pd.read_csv('../datasets/music.csv')\n",
    "music_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9516379a",
   "metadata": {},
   "source": [
    "Создаем значения для вводных данных (возраст и пол) и для выходных данных (жанр музыки), строим по ним предсказательную модель."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ab3ffbfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = music_data.drop(columns = ['genre'])\n",
    "Y = music_data['genre']\n",
    "\n",
    "model = DecisionTreeClassifier().fit(X.values, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbebc398",
   "metadata": {},
   "source": [
    "Подаем на вход 22 летнего человека с полом 0, которого нет в начальных данных, и просим предсказать его любимый жанр музыки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "974e11e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dance'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([ [22, 0] ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "927aac56",
   "metadata": {},
   "source": [
    "Тоже самое, только подаём на вход человека с возрастом 21 и полом 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25dfb3cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HipHop'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([ [21, 1] ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "995b6fc5",
   "metadata": {},
   "source": [
    "### Вычисление точности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "996f141f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8bcac7d",
   "metadata": {},
   "source": [
    "Разбиваем начальные данные на две категории: данные для обучения модели и для её тестирования"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "decd0338",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X.values, Y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7347374",
   "metadata": {},
   "source": [
    "Тренируем нашу модель и делаем предсказание "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e066bd5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['HipHop' 'Acoustic' 'Acoustic' 'Dance']\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier().fit(X_train, Y_train)\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2e3c043",
   "metadata": {},
   "source": [
    "Опираясь на тестовые данные, оцениваем точность нашего предсказания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31a1dfdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = accuracy_score(Y_test, predictions)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "471a0b65",
   "metadata": {},
   "source": [
    "### Сохранение и загрузка моделей"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8ed111",
   "metadata": {},
   "source": [
    "Данных может быть очень много и времени на обучение модели может уйти много, поэтому нужно понимать как сохранять и загружать модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dc572658",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca345e44",
   "metadata": {},
   "source": [
    "Сохранение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f206f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../models/music-recommender.joblib']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model, '../models/music-recommender.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bc825e9",
   "metadata": {},
   "source": [
    "Загрузка и предсказание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1bf5780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dance'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model = joblib.load('../models/music-recommender.joblib')\n",
    "loaded_model.predict([ [22, 0] ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c4fbba",
   "metadata": {},
   "source": [
    "### Визуализация древа выбора"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1030c683",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5682a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = tree.plot_tree(\n",
    "    model, \n",
    "    feature_names = [ 'age', 'gender' ],\n",
    "    class_names = sorted(Y.unique()),\n",
    "    label = 'all',\n",
    "    rounded = True,\n",
    "    filled = True\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
